Human-in-the-loop optimization of visual prosthetic stimulation

Retinal prostheses are a promising strategy to restore sight to patients with retinal degenerative diseases. These devices compensate for the loss of photoreceptors by electrically stimulating neurons in the retina. Currently, the visual function that can be recovered with such devices is very limited. This is due, in part, to current spread, unintended axonal activation, and the limited resolution of existing devices. Here we show, using a recent model of prosthetic vision, that optimizing how visual stimuli are encoded by the device can help overcome some of these limitations, leading to dramatic improvements in visual perception. We propose a strategy to do this in practice, using patients' feedback in a visual task. The main challenge of our approach comes from the fact that, typically, one only has access to a limited number of noisy responses from patients. We propose two ways to deal with this: first, we use a model of prosthetic vision to constrain and simplify the optimisation; second, we use preferential Bayesian optimisation to efficiently learn the encoder using minimal trials. As a proof-of concept, we presented healthy subjects with visual stimuli generated by a recent model of prosthetic vision, to replicate the perceptual experience of patients fitted with an implant. Our optimisation procedure led to significant and robust improvements in perceived image quality, that transferred to increased performance in other tasks. Importantly, our strategy is agnostic to the type of prosthesis and thus could readily be implemented in existing implants.

Effects of Depth-Based Object Isolation in Simulated Retinal Prosthetic Vision

Visual retinal prostheses aim to restore vision for blind individuals who suffer from outer retinal degenerative diseases, such as retinitis pigmentosa and age-related macular degeneration. Perception through retinal prostheses is very limited, but it can be improved by applying object isolation. We used an object isolation algorithm based on integral imaging to isolate objects of interest according to their depth from the camera and applied image processing manipulation to the isolated-object images. Subsequently, we applied a spatial prosthetic vision simulation that converted the isolated-object images to phosphene images. We compared the phosphene images for two types of input images, the original image (before applying object isolation), and the isolated-object image to illustrate the effects of object isolation on simulated prosthetic vision without and with multiple spatial variations of phosphenes, such as size and shape variations, spatial shifts, and dropout rate. The results show an improvement in the perceived shape, contrast, and dynamic range (number of gray levels) of objects in the phosphene image.

8-Channel Biphasic Current Stimulator Optimized for Retinal Prostheses

Retinal prostheses substitute the functionality of damaged photoreceptors by electrically stimulating retinal ganglion cells (RGCs). RGCs, densely packed in a small region, needs a high spatial resolution of the microelectrode, which in turn raises its impedance. Therefore, the high output impedance circuit and the high compliance output voltage are the key characteristics of the current-source-based stimulator. Also, as the system is intended to implant in the retina, the stimulation parameter should be optimized for efficiency and safety. Here we designed 8-channel neural stimulator customized to the retinal ganglion cell. Designed IC is fabricated in the TSMC 0.18 μm 1P6M RF CMOS process with 3.3 V supply voltage, occupying the 1060 μm×950 μm area.

Retinal Prosthesis in the Treatment of Retinitis Pigmentosa

Retinal prostheses are devices that receive an environmental visual stimulus, process it, and stimulate the degenerated retinal areas in order to produce a functionally efficient visual perception. Indications for implantation of these devices include hereditary retinal degenerations like retinitis pigmentosa, choroideremia, rod-cone dystrophy, and acquired macular diseases like geographic atrophy or fibrosis due to age-related macular degeneration. Clinically applied retinal prosthesis approaches can be classified as; epiretinal, subretinal, suprachoroidal, and scleral (transscleral suprachoroidal). In this paper, approaches of retinal prosthesis research groups, results of clinical trials, and the latest advances in their projects will be provided.

Treatments Being Developed in Retinitis Pigmentosa

Recent developments in understanding the pathophysiology of retinitis pigmentosa (RP) have shown that there have been new hopes in the treatment of this disease which can cause severe vision loss. Proven therapy for RP-associated photoreceptor loss and retinal pigment epithelial damage has not yet been reported. New or experimental approaches for the treatment of RP include platelet-rich plasma, gene therapy, transplantation of fetal retinal cells or stem cells, and electronic retinal prostheses.

Explainable AI for Retinal Prostheses: Predicting Electrode Deactivation from Routine Clinical Measures

To provide appropriate levels of stimulation, retinal prostheses must be calibrated to an individual’s perceptual thresholds (‘system fitting’). Nonfunctional electrodes may then be deactivated to reduce power consumption and improve visual outcomes. However, thresholds vary drastically not just across electrodes but also over time, thus calling for a more flexible electrode deactivation strategy. Here we present an explainable artificial intelligence (XAI) model fit on a large longitudinal dataset that can 1) predict at which point in time the manufacturer chose to deactivate an electrode as a function of routine clinical measures (‘predictors’) and 2) reveal which of these predictors were most important. The model predicted electrode deactivation from clinical data with 60.8% accuracy. Performance increased to 75.3% with system fitting data, and to 84% when thresholds from follow-up examinations were available. The model further identified subject age and time since blindness onset as important predictors of electrode deactivation. An accurate XAI model of electrode deactivation that relies on routine clinical measures may benefit both the retinal implant and wider neuroprosthetics communities.

Photovoltaic retinal prosthesis restores high-resolution responses to single-pixel stimulation in blind retinas

Retinal prostheses hold the promise of restoring vision in totally blind people. However, a decade of clinical trials highlighted quantitative limitations hampering the possibility of reaching this goal. A key challenge in retinal stimulation is to independently activate retinal neurons over a large portion of the subject’s visual field. Reaching such a goal would significantly improve the perception accuracy in retinal implants’ users, along with their spatial cognition, attention, ambient mapping and interaction with the environment. Here we show a wide-field, high-density and high-resolution photovoltaic epiretinal prosthesis for artificial vision (POLYRETINA). The prosthesis embeds 10,498 physically and functionally independent photovoltaic pixels, allowing for wide retinal coverage and high-resolution stimulation. Single-pixel illumination reproducibly induced network-mediated responses from retinal ganglion cells at safe irradiance levels. Furthermore, POLYRETINA allowed response discrimination with a high spatial resolution equivalent to the pixel pitch (120 µm) thanks to the network-mediated stimulation mechanism. This approach could allow mid-peripheral artificial vision in patients with retinitis pigmentosa.